Google Cloud Certification: Cloud Data Engineer Professional Certificate
Investigate the objectives and advantages of Google's Big Data and Machine Learning products, including the use of BigQuery for interactive analysis, Cloud SQL, and Dataproc for migrating MySQL and Hadoop applications, and the selection of a variety of data processing tools on Google Cloud.
Description for Google Cloud Certification: Cloud Data Engineer Professional Certificate
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On Coursera provided by Google Cloud
Duration: 1 month at 10 hours a week
Schedule: Flexible
Pricing for Google Cloud Certification: Cloud Data Engineer Professional Certificate
Use Cases for Google Cloud Certification: Cloud Data Engineer Professional Certificate
FAQs for Google Cloud Certification: Cloud Data Engineer Professional Certificate
Reviews for Google Cloud Certification: Cloud Data Engineer Professional Certificate
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Google Cloud Certification: Cloud Data Engineer Professional Certificate
Begin your vocation in generative AI data engineering. Obtain the necessary skills to secure a position as a data engineer by acquiring an understanding of generative AI. No prior experience is required.
This learning path provides a thorough overview of generative AI. This specialization delves into the ethical considerations that are essential for the responsible development and deployment of AI, as well as the foundations of large language models (LLMs) and their diverse applications.
Learn machine learning with Google Cloud. End-to-end machine learning experimentation in the real world
Learn how to use Gemini for Google Workspace to boost productivity and efficiency in Gmail through its generative AI features.
Learn to build machine learning solutions using Generative AI on AWS, including an understanding of AWS cloud computing and utilizing services like Amazon Bedrock.
Acquire the skills necessary to program powerful systems in Rust. Through projects in data engineering, Linux tools, DevOps, LLMs, Cloud Computing, and machine learning operations, acquire the skills necessary to develop software that is both efficient and robust, utilizing Rust's distinctive safety and speed.
Become an adept in the field of machine learning. Break into the field of artificial intelligence by mastering the fundamentals of deep learning. Recently upgraded with state-of-the-art methodologies!
Gain the skills needed for a machine learning engineering role and prepare for the Google Cloud Professional Machine Learning Engineer certification exam by learning to design, build, and productize ML models using Google Cloud technologies.
Gain practical experience in optimizing, deploying, and scaling machine learning models using Google Cloud Platform through a structured five-course specialization with hands-on labs and a focus on advanced topics and recommendation systems.
Become a machine learning engineer. Enhance your programming abilities with MLOps
Featured Tools
Students who complete the course will have the knowledge they need to use Amazon Q for data analysis, software development, task automation, and organizational customisation.
The course delves into the development of effective prompts for AI communication, the use of ChatGPT and DALL-E in workflow processes, and the assurance of accurate aesthetic representation.
Learn to use Databricks and MLlib for creating and advancing machine learning models with Spark.
Learn to create responsive websites using HTML, CSS, JavaScript, and React, utilize the Bootstrap framework, collaborate with GitHub, and prepare for coding interviews with portfolio-ready projects.
This course teaches aspiring data scientists to train and compare classification models using supervised machine learning techniques, focusing on practical applications and best practices.